{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Demonstration of the ear clip module\n",
    "\n",
    "This module is the code from mrbaozi's repo https://github.com/mrbaozi/triangulation, simply turned into a module for ease of use. All credit to him.\n",
    "\n",
    "To use, simply build a list of lists, each inner list is a coordinate pair of one of the points on your simple polygon. Pass this to the triangulate method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0063d36048>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6AzpdH3195vve3oHrvj4Der353Nc3cDb06uk19KAz6ND19dBQ32CtR01NDf/4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0063d54ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from earclip import triangulate\n",
    "%matplotlib inline\n",
    "\n",
    "poly = [[ 229.28340553,   78.91250014],\n",
    "       [ 258.42948809,   17.98278109],\n",
    "       [ 132.01956999,  -22.96900817],\n",
    "       [ 107.97774096,   23.39276058],\n",
    "       [  65.85573925,   28.63846858],\n",
    "       [  41.66373597,  -92.78859248],\n",
    "       [  -5.59948763,  -54.18987786],\n",
    "       [ -44.61508682,  -69.7461117 ],\n",
    "       [ -28.41208894, -106.93810071],\n",
    "       [ -71.11899145, -125.56044277],\n",
    "       [-100.84787818,  -88.51853387],\n",
    "       [-211.53564549, -160.76853269],\n",
    "       [-244.22754588, -147.51172179],\n",
    "       [-226.83717643,  -42.0984372 ],\n",
    "       [-230.65279618,  -10.5455196 ],\n",
    "       [-240.50239817,   70.87826746],\n",
    "       [ -12.48219264,  137.70176109],\n",
    "       [   4.65848369,  204.21077075],\n",
    "       [ 176.5243417 ,  193.73497584],\n",
    "       [ 171.13537712,   87.27009315]]\n",
    "\n",
    "#Plot the polygons and its inside triangles\n",
    "for i in range(len(poly)):\n",
    "    for j in range(2):\n",
    "        poly[i][j] = poly[i][j]*1000.0\n",
    "tris = triangulate(poly)\n",
    "c = ['r','g','b','k','g','y']\n",
    "i = 0\n",
    "for tri in tris:\n",
    "    plt.plot([tri[0][0],tri[1][0],tri[2][0],tri[0][0]],[tri[0][1],tri[1][1],tri[2][1],tri[0][1]],c=c[i % 5],alpha=0.5)\n",
    "    i+=1\n",
    "        \n",
    "poly.append(poly[0]) #append the first point, to close the polygon for drawing\n",
    "plt.plot([p[0] for p in poly],[p[1] for p in poly],'k-',lw=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
